Recall assumption (2), in order to incorporate lexical, structural and semantic information for the similarity calculation of two SSRs holding the same discourse relation, three types of matches were defined for  Intuitively, a simple way of estimating the similarity between two SSRs was using the number of mismatches.

we utilized match( where i ̸ = j, which integrated the three types of matches defined above to calculate the number of mismatches and generate common SSRs.


In other words, the number of mismatches in match(d i , d j ) could not precisely reflect the confidence of the generated common SSRs.

it was needed to weigh different mismatches for the confidence calculation of common SSRs.


In addition, adjectives and adverbs appearing in sentiment lexicons were important for polarity classification.

for these 4 kinds of words, we utilized −1.0 for a mismatch and −0.50 for a partial match.


The best performance was observed when minconf =−3.5.

−3.5 was utilized as the threshold value for cSSR in the following experiments.


Comparing to the weights of mismatches for most representative words (−1.0), the proposed method successfully down weighed the words which were not important for discourse identification.

weighing and filtering were able to preserve high quality SSRs while filter out low quality SSRs by setting the confidence threshold, i.e.


For example: The first segment of example (b) was negative ("banned" expressed a negative sentiment) and a Continuation relation held between these two seg-ments.

the polarity of the second segment should be negative.


5 In contrast, our test sentences from the web contain approximately 10% imperatives.

parsers trained exclusively on the WSJ underproduce imperative parses, especially a case-sensitive version of the baseline.


In the shift-reduce parser, targeted self-training gives higher reordering scores than training on the treebank, and in BerkeleyParser, the opposite is true.

both approaches produce similarly good results.


Table 4 shows that this gives reordering scores similar to those of our best parsers.

it is at least possible that the targeted self-training process could have resulted in a parser that achieves high reordering score by producing parses that look like those in the QuestionBank.


Evaluating this objective involves training the other two models.

we can only hope to optimize this objective directly over a small dimensional space, for instance using a grid search.


In 0-1 matching, the rule's source side must contain only one syntactic category, but a rule may have been decorated with more than one syntactic category on the source side.

we have to choose the most reliable category and discard the others.


However, a major deficiency of this algorithm is that it does not make full use of the source side syntax, since it retains only the most frequent SAMT-style syntactic category to describe the rule's source syntax.

this algorithm penalizes all the other categories equally, although some may be more frequent than others, as in the case of DEG and DEV in the rule To some extent, the likelihood matching algorithm solves the main problem of 0-1 matching.


Conventional dependency structure based models (Lin, 2004;Quirk et al., 2005;Ding and Palmer, 2005;Xiong et al., 2007) typically employ both substitution and insertion operation to complete translations, which make it difficult to specify ordering information directly in the translation rules.

they have to resort to either heuristics (Lin, 2004;Xiong et al., 2007) or separate ordering models (Quirk et al., 2005;Ding and Palmer, 2005) to control the word order of translations.


While this yields more involved subproblems (with a quadratic term), we show that exact solutions can still be efficiently computed for all cases of interest, by using sort operations.

we obtain parsers that can handle very rich features, do not require specifying a decomposition, and can be heavily parallelized.


Update Procedure: When a new word pair "x" with count c arrives, one counter in each row (as decided by its corresponding hash function) is updated by c. Query Procedure: Since multiple word pairs can get hashed to the same position, the frequency stored by each position is guaranteed to overestimate the true count.

to answer the point query for a given word pair, we return minimum over all the positions indexed by the k hash functions.


Next, we build on the intuition that event predicates appearing in a large number of documents are probably not important or discriminative.

we penalize these predicates when calculating s pp by adopting the inverse document frequency (idf): 1+N , D is the total number of documents in the collection and N is the number of documents that p occurs in.


Generally, for words which have been used in this discourse, the magnitude of probabilities in the 'seen before' distribution are much higher than in the 'discourse new' distribution.

there is a strong bias to classify NPs which match word-for-word as being co-referent.


First, we restrict the cognate assignments to stay within a gloss.

there are many fewer potential matchings to consider.


In this example, at least two changes of state are required to explain the data: both C and B must have evolved from A.

the parsimony score for this tree is two.


We have a nearly tree-structured graphical model ( Figure 1); it is not a tree only because of the innovation parameters.

we apply the common trick of grouping variables to form a tree.


Even in models that would be tractable with "ordinary" messages, inference with automata quickly becomes intractable, because the size of the automata grow exponentially with the number of messages passed.


Crucially, these cognate groups do not follow the phylogeny closely.

either there was a significant amount of borrowing between these languages, or there was a striking amount of homoplasy in Proto-Formosan, or these words are in fact mostly cognate.


Furthermore, this system combination works better than the conventional multi-model approach by up to 0.7 BLEU on both the dev and test sets.

a domain-adapted system comprising two phrase tables trained on a total of 180k sentences outperformed the standard multi-model system which was trained on 12 million sentences.


Similarly, the test data are 2489 four-way translated sentences.

all MT evaluations involve only single references.


Every system had at least one virtually unanalyzed phenomenon.

we have shown that the dependencies being missed by typical modern approaches to parsing are more varied and more numerous than previously thought.


But, actually, measures such as ROUGE or BLEU are not proper "metrics", because they do not satisfy the symmetry and the triangle inequality properties.


In our experiments, we take the sum of adequacy and fluency, both in a 1-5 scale, as a global measure of quality (LDC, 2005).


This result has a key implication: Corroborating evaluation results with a new measure, even when it has lower correlation with human judgements, increases the reliability of results.

if the correlation with judgements is not determinant, the question is now what factor determines the contribution of the new measures.


Let us define the heterogeneity H(X) of a set of measures X as, given two system outputs s and s such that g = s = s = g (g is the reference text), the probability that there exist two measures that contradict each other.

that is: given a set X of measures, the property states that there exists a strict growing function F such that: In other words, the more the similarity measures tend to contradict each other, the more a unanimous improvement over all similarity measures is reliable.


According to the Additive Reliability property, this joining implies a reliability increase.

the higher the heterogeneity, the higher the minimum Reliability achieved by the corresponding measure sets.


According to the first constraint of similarity measures, a text that is equal to the reference achieves the maximum score: Finally, if we assume that the reference (human produced texts) has a maximum quality, then it will have equal or higher quality than the other output.

the reliability of the measure set is maximal.


As such, if a statistic based on an automatic metric, such as ROUGE-2, were to show fewer systems performing at human level of performance than the statistic of averaging scores, such a statistic would be preferable because 3 Assessors have commented privately at the Text Analysis Conference 2008, that while the origin of the summary is hidden from them, "we know which ones are machine generated."

automatic summarization fails the Turing test of machine intelligence (Turing, 1950).


Thus, we demonstrated that the Wilcoxon paired test shows more statistical power than the t-test for comparing summarization systems.

the use of paired testing should not only be used in formal evaluations such as TAC, but also should be employed by summarization developers to more accurately assess whether changes to an automatic system give rise to improved performance.


If no configurations are present between any pair of words, the "other" feature fires.

only one configuration feature fires for each phrase dependency attachment.


Given a permutation π of the indices 1..n, let the candidate reordering be w π 1 , w π 2 , ..., w πn .

π i denotes the index of the word in the source sentence that maps to position i in the candidate reordering.


We will use a bracket notation to describe trees or treelets.

"a(b c d(e))" is the tree at the bottom of figure 2.


More precisely, we want to retrieve efficiently treelets containing at least one leaf of the query tree.

an alternative computation strategy would only explore treelets containing at least one terminal node.


For each treelet of size m we would have had in a single label query tree, we now virtually have 2 m treelets.

it is not reasonable in general to try to compute all these treelets.


It is not clear why this cluster contains both types of words, but many of the high ranking words beyond the top ten do correspond to treatments, such as "Morphine", "Albumin", "Ativan", and "Tylenol".

the discovered argument classes show some similarity to the ones chosen by annotators.


Generally, specialized rules have much smaller recall than unspecialized rules.

the PRA approach achieves high recall partially by combining a large number of unspecialized paths, which correspond to unspecialized rules.


If young co-occurs with mature in a different pseudodocument (AGE might be a candidate), this results in a situation where (i) young and mature share one or more latent topics and (ii) the topic proportions for the attributes MATURITY and AGE will become similar to the extent of common words in their pseudodocuments.

the final attribute model is expected to assign a (small) positive probability to the relation between mature and MATURITY without observing it in the training data.


Moreover, since C-LDA collects pseudo-documents focused on individual target attributes, we are able to link external categories to the generative process by heuristically labeling pseudo-documents with their respective attribute as target category.

we approximate P (w|a), the probability of a word given an attribute, by P (w|d) as obtained from LDA: 2 The dependency paths, together with the set of attribute nouns of interest, have to be manually specified.


Available from: http:// wordnetcode.princeton.edu/standoff-files/ core-wordnet.txt 5 If an attribute provides only one example, this was added to the development set.

the test set only comprises Training data.


Accordingly, we hypothesize that if we know the semantics of words in advance, we can get a better indication of their topics.

in this paper, we test our hypothesis by exploring the integration of word semantics explicitly in the topic modeling framework.


The window size is fixed for each word: it contains /2 preceding words, and /2 following words.

a word in the original document will have copies, existing in + 1 local windows.


Intuitively, a sense definition reveals some prior knowledge on the topic domain: the definition of sense [crime, offense, offence] indicates a legal topic; the definition of sense [basketball] indicates a sports topic, etc.

during inference, we want to choose a topic/sense pair for each word, such that the topic is supported by the context θ and the sense definition also matches that topic.


We normalize definition length by a variable γ.

before the topic model sees the actual documents, each sense s has been sampled γ times.


On the other hand, it can be imagined that the set of values {z ij , s ij |j − /2 ≤ i ≤ j + /2} in different windows v i should roughly be the same, since they are hidden values for the same word w j .

to reduce computation complexity during Gibbs sampling, we approximate the values of {z ij , s ij | i = j} by the topic/sense (z jj , s jj ) that are generated from window v j .


In Experiment II (Section 5) we showed that lengthening is strongly associated with sentiment.

words which are lengthened can provide us with good candidates for inclusion in the lexicon.


In the simplest case, we might have pair-wise constraints on whether two pieces of text have the same viewpoints (i.e., liked or disliked by the same rater), which would yield a different learning problem akin to the metric learning problem; note, however, the complication that two pieces of text receiving different labels from a given user might not necessarily contain contrasting viewpoints.

rather than trying to reduce this problem to a set of known text classification tasks, we address this task via a collaborative filtering framework that incorporates textual features.


Model specification: We work in a generalized linear model framework (McCullagh and Nelder, 1989) that assumes µ ij (or some monotone function h of µ ij ) is an additive function of (1) the rater bias α i of user i since some users may have a tendency of rating comments more positively or negatively than others, (2) popularity β j of comment j, which could reflect the quality of the comment in this setting, and (3) the author reputation γ a(j) of user a(j) since comments by a reputed author may in general get more positive ratings.

the overall bias is In addition to the bias, we include terms that capture interactions among entities (raters, authors, comments).


For the purposes of this paper, we limit the data set to only the first two utterances from each conversation.

of this constraint, any system trained with this data will be specialized for responding to Twitter status posts.


We also make the simplifying assumption that all citations occur at the end of each year.

the forecast gap is only  Table 4: Classification accuracy (%) for predicting whether ACL papers will be cited within three years. "


Although a line of research attempts to identify unknown words on the fly (Uchimoto et al., 2001;Asahara and Matsumoto, 2004), it by no means provides a definitive solution because it suffers from locality of contextual information available for identification (Nakagawa and Matsumoto, 2006).

we like to perform separate lexical acquisition processes in which wider context can be examined.


Natural languages are especially interpreted to have a hierarchy of sequential structures on different levels of abstraction.

many tasks in NLP are modeled as a cascade of sequence predictions.


However, it puts the distribution of states into Bayesian networks with cyclic dependencies, and exact inference is not tractable in such a model in general.

it requires some approximate inference algorithms in learning or predictions.


Unfortunately, the existing datasets do not allow this since they are tied to one particular granularity; moreover, they only contain a few hundred NCs.

our objective is to build a large-scale dataset of hundreds of thousands of NCs, each interpreted (1) by an abstract semantic relation and (2) by a set of paraphrasing verbs.


For example, student loan is "loan given to a student", while morning tea can be paraphrased as "tea in the morning".

many NLP approaches to NC semantics have used verbs and prepositions as a fine-grained semantic representation or as features when predicting coarse-grained abstract relations.


For example, malaria mosquito, which expresses CAUSE, can be paraphrased using verbs like carry, which do not imply direct causation.

while we will be focusing on extracting NCs for a particular abstract relation, we are interested in building semantic representations that are specific for these NCs and do not necessarily apply to all instances of that relation.


Seed NCs: bronze statue, cable network, candy cigarette, chocolate bar, concrete desert, copper coin, daisy chain, glass eye, immigrant minority, mountain range, paper money, plastic toy, sand dune, steel helmet, stone tool, student committee, sugar cube, warrior castle, water drop, worker team Seed patterns: be composed of, be comprised of, be inhabited by, be lived in by, be made from, be made of, be made up of, be manufactured from, be printed on, consist of, contain, have, house, include, involve, look like, resemble, taste like As we can see, the most frequent patterns are of highest quality, e.g., be made of (16), while the less frequent ones can be wrong, e.g., serve (1).

we filtered out all verbs that were proposed less than five times with the 20 seed NCs.


As defined in (Collins and Duffy, 2002), a syntactic tree fragment f r i is active in t i when f r i is a subtree of the syntactic interpretation of t i .

these features represent ground rules connecting the left-hand sides and the right-hand sides of the tweet pair: each feature is active for a pair (t 1 , t 2 ) when the left-hand side f r 1 is activated by the syntactic analysis of t 1 and the right-hand side f r 2 is activated by t 2 .


This feature space models the relations between words syntactically.

it overcomes the limitations of the LEX feature space.


Entailment pairs composed of the original sentences (ENG) and the modified ones (ENG1) are used as input of (monolingual) annotation HITs asking Turkers to decide which of the two texts contains more information.

each ENG/ENG1 pair is annotated as an example of uni-/bidirectional entailment, and stored in the monolingual English corpus.


These HITs are combined in an iterative process that alternates text generation, grammaticality check, and entailment annotation steps.

for each original ENG text we obtain multiple ENG1 variants of the three types (paraphrases, more general texts, and more specific texts) and, in turn, a set of annotated monolingual (ENG/ENG1) TE pairs.


We generated abstractness ratings for a large vocabulary of 114,501 words in order to maximize the variety of text genres and the range of applications for which our list of abstractness ratings would be useful.

of this large vocabulary, many of the words in Table 2 are rare and obscure; however, the measure of quality of the algorithm is the correlation with the testing set (0.81), not the familiarity of the words in the table.


The minority class will then always have a precision of 100%.

we use a modified f-score in which the precision of a class is 0% if the algorithm never guesses that class.


Following on, the word may is likely to be a month rather than a modal verb, although the latter is more frequent and will dominate in BO .

we have much less faith inp(w i |BO ) than iñ p(w i |BO ) and would like a much smaller weight γ assigned to BO .


It would be infeasible to apply a model consisting of more than a handful of such trees without distributed computing of some sort.

we pose the following question: If we can afford to have only a handful of trees in the model, what would be best approach to construct those trees?


Unfortunately, the text entry rates provided by AAC devices are typically low, between 0.5 and 16 words-per-minute (Trnka et al., 2009).

researchers have made numerous efforts to increase AAC text entry rates by employing a variety of improved language modeling techniques.


As shown in figure 3, word-for-word the TURK-TRAIN data was superior for our three most AAClike test sets.

it appears our crowdsourcing procedure was successful at generating AAC-like data.


Our data does not contain long-term two-sided conversations.

it may not be as useful for evaluating techniques that adapt to past messages or that use the conversation partner's communications.


When applied to sequences and tree structures, the kernels discussed above produce many different kinds of features.

the design of appropriate syntactic/semantic structures determines the representational power of the kernels.


In contrast, we have shown that STK-CT is not effective for our domain, as it presents very innovative elements: questions in affirmative and highly variable format.

we employed new methods such as PTK, dependency structures, multiple sequence kernels including category information and many combinations.


We first introduce a new instantiation of the French Treebank that, unlike previous work, does not use gold MWE pre-grouping.

our experimental results also provide a better baseline for parsing raw French text.


Almost all work on the FTB has followed A -C and used gold MWE pre-grouping.

most results for French parsing are analogous to early results for Chinese, which used gold word segmentation, and Arabic, which used gold clitic segmentation.


However, the two sites conflict since the probabilities of setting b s1 and b s2 both depend on counts for the tree fragment rooted at NP.

sites (1) and(2) are not exchangeable: the probabilities of their assignments depend on the order in which they are sampled.


Whereas some potential connectives such as the Arabic connective /lkn/but almost always have discourse usage, this is not true for all potential connectives.

1 the discourse usage of Arabic /rġbh/desire needs to be distinguished from its use as a noun.


6 In contrast, out of the 3073 adjacent sentence pairs in our corpus, 2140 (70%) were linked by an explicit connective, 948 (30%) were linked via non-wa connectives.

for our corpus, modeling of explicit connectives is primary: intrasentential relations tend to be marked by connectives anyway in both English and Arabic, and our corpus shows that this is true for most local intersentential relations as well.


This advantage of our model over the connectiveonly model might disappear if in a larger corpus more instances of those connectives are found and are still unambiguous.

we are more interested in how our classifier does on truly ambiguous connectives (33 connective types accounting for 3239 tokens of 3813 overall tokens).


The connective /f/then is distinguished into EXPANSION.EXEMPLIFICATION, CONTINGENCY.CAUSE.RESULT and CONTIN-GENCY.CAUSE.REASON readings, depending on the lexemes around it, the parents of its arguments, and whether its argument 2 is tensed or not.

nontensed arguments are most often nominalisations leading to a reason reading, whereas a verb phrase as argument 2 and a sentence as argument 1 often is a result reading.


For example, the following sentence might say "The cat was diagnosed by a previous vet but brought to me due to new complications" or "The cat was diagnosed with it 8 years ago as a kitten in the animal shelter".

we were very conservative in labelling sentences as Representative speech acts.


Precision improves for Commissives by an additional 7% and Representatives by 2% when using only these lexical and syntactic features.

we use the minimal lexsyn features for the rest of our experiments.


The single query may not be formulated with the best terms or the query itself may not contain comprehensive information required for a highperformance ranking algorithm.

techniques of query expansion or query reformulation can be introduced and previous research has shown the effectiveness of those techniques in such applications as information retrieval and question answering (Manning et al., 2008;Riezler et al., 2007).


(2) our top 4 supervised rankers apply almost the same set of features, while different KBP2009 systems may apply more diversified features.

diversity is a highly important factor that makes MaCR approach effective.


For example, the phrase "Grammy Award winner" associated with entity Jimmy Page may occur only in the form "Grammy winner" near some mention "Page".

our algorithm for the similarity of mention m with regard to entity e computes partial matches of e's keyphrases in the text.


Part of the reason may be that by using only named entities in the context rather than all words, we have effectively picked the most useful contextual terms.

positional feedback models do have exhibit much benefit for our problem.


These methods leveraged heavily on the entity types of candidates when assigning them to template slots.

potentially different semantic roles of candidates having the same entity type could become indistinguishable (Sudo et al., 2003;Sekine, 2006).


In Figure 5, the histograms for the pattern based methods CDP and Espresso start around 1000 noun pairs, which is far above this new lowerbound.

pattern based methods naturally tend to induce patterns that are much more frequent than the range of patterns our method can capture, and it is unlikely that this is a result of implementation details like pattern frequency threshold.


However, not all the dictionaries provide this kind of information.

external knowledge needs to be used in order to guide mapping according to sense.


This work focuses on adequate approaches for less resourced languages.

the assumption for the experimentation is that few resources are available for both source and target languages.


In addition, DS offers much better recall of translations and entries.

dS performs the best if both precision and recall are taken into account by F-score.


Cohesion penalty counts the total occurrences of unresolved words for a translation hypothesis, which involve newly encountered unresolved words as well as old unresolved words carried on from the derivation history.

the cohesion penalty is "stateful", i.e., an unresolved word is repeatedly penalized until it gets resolved.


Our intuitive interpretation is that the target dependency language model capitalizes on two characteristics of the dependency structure: it is based on words and it directly connects head and child.

the target dependency language model makes good use of the dependency representation as well as the target side training data.


1 However, the complexity of generating this translation forest is up to O(n 6 ), because we still need biparsing to create the reference derivations.

we propose a method to fast generate a subset of the forest.


We use a translation forest that contains both "reference" derivations that potentially yield the reference translation and also some neighboring "nonreference" derivations that fail to produce the reference translation.

our forest only represents some of the derivations for a sentence given an SCFG rule table.


Although there is a two-level loop for visiting nodes (line 3 and 6), each node is visited only one time in the inner loops.

the complexity is linear with the number of nodes #node.


Formula (3) accounts for the interaction between r and sub-items ι 1 and ι 2 , but it does not integrate the cost relative to the interaction between the item and the surrounding context.

the item score computed in a bottom-up fashion is an approximation of the score the item has in a broader context.


This ordering constraint implies that full yield information is available when an item is processed, but information about ancestors and siblings is missing.

non-bottomup context cannot be used because of the ordering constraint.


Table 4 shows that employing this trick can further improve the performance by 0.15 in BLEU_W.

the cache-based approach significantly improve the performance by 0.81 (***) in BLEU_W over Moses.


All of these values are taken into account in (4), regardless of the loss that they would incur if they were evaluated for translation quality relative to the missing x i .

φ does not need to be trained to minimize the risk itself (so there is no circularity).


The above three association measures use the same training data that is available to compute the covariance matrices in CCA.

their utility in bringing additional information, which is not captured by the covariance matrices, is arguable (our experiments show that they are indeed helpful).


In both the above experiments, the performance bars are very similar when we use MI instead of Yule and vice versa for weighting monolingual word pairs.

to illustrate the main ideas we chose Yule's ω for the former combination and MI for the latter combination.


First note that the primal problem is a strictly convex function over a compact convex domain.


Concretely, we define Recall that Z = n j=1 m j u j e α j .

from (3) we can rewrite p j = u j e α j /Z.


Needless to say though that all the results we obtained thus far are still valid.

let us consider a point (ν, µ) on the boundary between I 0 and I + , namely, there exist a line ℓ +i such that, By definition, for any j ∈ I 0 we have q i < q j for all j ∈ I 0 which implies that Summing over j ∈ I 0 we get that and we must be moving "up" from I 0 to I + when the line ℓ 0 hits ℓ i .


In this section, we argue that paraphrases of katakana noun compounds provides useful information on word boundaries.

we propose using paraphrase frequencies as features for training the discriminative model.


Both "finish book" and "discuss book" are coercions (and logical metonymies) themselves, and do not form a valid interpretation.

4 when discovering interpretations for logical metonymies, we must be aware of the selectional restrictions of candidate interpretations.


With predicate induction, LDA outperforms cLDA on only one metric for 25 classes and five metrics for 50 classes.

the induced predicates do reduce the negative impact caused by the single selected class assumption for semantic class induction.


In the "piano" example (Section 3.2), the concept piano is highly specific due to its large number of siblings under the parent node percussion instruments.

the density of its child nodes upright piano and grand piano should inherit its specificity on top of their own.


we only report NMI when the number of classes in clustering and gold-standard is substantially different.


Recall that the constrained version of HGFC learns the upper levels of classification on the basis of soft constraints set at the bottom level, as described earlier in section 3.2.4.

nMI and F are both greater than 90% at the bottom level and the results at the top level are notably lower because the impact of the constraints degrades the further away one moves from the bottom level.


Given the two child sequences s 1 a = c n 1 and s 2 b = c n 2 (a and b are the last children) where s rewrite the relation as: Note that D p satisfies the recursive relation: By means of the above relation, we can compute the child subsequences of two sequences s 1 and s 2 in O(p|s 1 ||s 2 |).

the worst case complexity of the SPTK is identical to PTK, i.e.


However, if the words are found to occur closer together than explainable by our null model, then we hypothesize a more direct association between the words.

this null model detects biases in span distributions of word-pairs while being agnostic to variations in global unigram frequencies.


Since π 20 (3, 4, 400) = 0.09, even if 3 of the 4 occurrences of α have span less than 20 words, there is 9% chance that the occurrences were a consequence of a random event.

if we desired a confidence-level of at least 95%, we would have to declare observed co-occurrences of α as insignificant.


Since each document in the corpus is assumed to be generated independently, z i 's are independent random variables and we can bound the deviation of the observed value of Z from its expectation using Hoeffding's Inequality -for any t > 0, we have Recall that Z models the number of documents supporting the hypothesis "α is an -significant wordpair.").

the upper-bound δ (= exp(−2Kt 2 )), 0 < δ < 1 denotes the upper-bound on the probability that just due to random chance, more than (E(Z) + Kt) documents out of K will support the hypothesis "α is an -significant word-pair."


Only if Z was 44 or more, there would be less than 1% chance of that being a random phenomena.

we cannot be 99% sure that the observed cooccurrences in the 33 documents are non-random.


If (j − i) < x, whenever there are k span-constrained occurrences in positions (j + 1) to , there will be (k + 1) span-constrained occurrences in positions 1 to .

we increment hist f, [k + 1] by the quantity hist f −1, −j [k] (lines 10-12).


We may manually assemble this list, but this could be a labor-intensive task.

we propose to acquire this kind of world knowledge automatically from annotated data.


To answer these questions, we need to better understand the kind of errors made by our approach.

we show in Table 4   best-performing information-status classifier, Base-line+Both.


Each word/lexeme is mapped to an appropriate synset in WordNet based on its sense and represented using the corresponding synset id of WordNet.

the word love is disambiguated and replaced by the identifier 21758160 which consists of a POS category identifier 2 followed by synset offset identifier 1758160.


The information content shared by two concepts A and B is given by their most specific subsumer (lowest superordinate(lso).

this metric defines the similarity between two concepts as Algorithm 1: Synset replacement using similarity metric Lesk: Each concept in WordNet is defined through gloss.


The reduction in the performance of SA for automatically annotated senses is because of the number of unmatched synsets.

although the accuracy of IWSD is currently 70%, the table indicates that IWSD can match the performance of manually annotated senses for SA if IWSD is able to tag correctly those top information content synsets.


Designing a general meaning representation which can capture the semantics of a spoken language is very complex.

in practice, the meaning representations depend on the specific application domain being modeled.


Due to the requirement of unlimited history, many optimization tricks for rescoring with feedforward-based NNLMs as presented by Schwenk (2007) cannot be applied during rescoring with RNN LM.

this model is a good candidate to show effectiveness and importance of our work.


The size of the neighborhood in our approach is a variable factor which depends upon the confusability in any particular region of the word lattice.

the local neighborhood are in some sense a function of the confusability present in the lattice rather than some predetermined factor.


out violating property 2 for the specific time marker.

the time complexity for checking property 1 is O(|V|) and that for property 2 is O(|T |×|E|), where |T | is the total number of unique time markers.


Usually |T | |E| and hence the time complexity for checking property 2 is almost linear in the number of edges.

effectively, the time complexity for cutting the lattice is O(|V| + |E|).


The output of the algorithm is a word string, W * , such that it is the concatenation of final decoded word strings from each sub lattice.

if we denote the final decoded path (under some decoding scheme, which will become apparent next) in the j th sub lattice by π * j and the concatenation symbol by '•', then Algorithm 1 Iterative Decoding on word lattices.


At the end of each iteration, CurrHypo is set to the concatenation of 1 best paths from each sub lattice while PrevHypo is set to the old value of CurrHypo.

if we are analyzing some i th sub-lattice in some iteration, then 1-best paths from all but this sub-lattice is kept fixed and a new 1-best path under the re-scoring LM is found out.


Once we have formed self contained sub lattices, we want to prune all but the top few best complete paths (obtained un-der baseline / starting model) of those sub lattices whose entropy is below some threshold.

believing in the original model's confidence, we want to focus only on those sub lattices which the recognizer found difficult to decode in the first pass.


The regularized MLE for the binary classifiers satisfies Notice the right hand sides of (4) and (7) are the same.

taking P (w|h) = P b (w|h) from ML trained binary classifiers gives an LM that meets the MLE constraints for language models.


We note that iterative estimation of the LM shown in (2) in general requires enumerating over the T training cases in the training set and computing the denominator of (2) for each case at a cost of O(V ).

each iteration of training takes O(V T ) in general.


However, as mentioned earlier, we are able to maximally subsample negative examples for each classifier.

the classifier for w is trained using the C(w) positive examples and a proportion α of the T − C(w) negative examples.


While the complexity remains the same without subsampling, the majority of the complexity comes from processing negatives examples for each binary classifier.

we can achieve the same level of speedup as standard subsampling by only subsampling negative examples, and most importantly, it allows us to keep all the existent patterns(positive examples) in the training data.


Furthermore, some important temporal modifier, numeric modifier and clausal complement need to be retained because they reflect content aspects of the summary.

we propose the "dependency parse and trim" approach, which prunes sentences based on dependency tree representations, using English grammatical relations to recognize clauses and remove redundant structures.


Ordinal numbers that were annotated as the use of the number attribute were re-tagged as spatial relations, as these usually described the position of the target within a line of landmarks.

of the removal of annotations not pertaining to the use of the four landmark attributes, 2785 referring expressions had no annotation left; we removed these instances from the final data set.


The count of trees containing span i, j is α(i, j) • β(i, j).

equation 1 can be rewritten as On condition that i, j is a span in the parse of e, the probability that i, j has two children i, k and k + 1, j is the probability that i, j is a span in the parse of e and has two children i, k and k + 1, j can be calculated as follows: Since each candidate projected span aligns to one treelet at most, this probability is also the frequency of the candidate projected production related to the three spans.


For example, the deictic determiner the can refer to a specific entity ("the FDA") or have a universal interpretation ("the products").

to disambiguate between de re and de dicto interpretations for determiners, we need two types of features -(1) Features to predict whether ambiguous and deictic determiners are universal or not, and (2) Features to determine the type of implicit determiners.


In most cases, the number of operators per preterminal is less than 7.

the total number of possible orderings is typically less than 7!, and a beam size of 10 4 is sufficient to compute an exact ordering.


The depth of a complete derivation d is the length of the longest path from the root to a leaf in the parse tree associated with d. The length of a path is defined as the number of nodes it visits.

if d = π for some rule π = (A → w) with w ∈ Σ * , then the depth of d is 2.


To extend the algorithm to support non-projective trees, we introduce move-right and move-left operations similar to the stack-to-buffer swaps proposed by Nivre (2009) for shift-reduce style parsing.

instead of attaching a token to one of its neighbors at each step, the algorithm may instead decide to move a token past one of its neighbors.


2 These objectives are plausible, yet both can be provably "wrong" (Spitkovsky et al., 2010a, §7.3).

it is permissible for lateen EM to maneuver between their gradients, for example by tacking around local attractors, in a zig-zag fashion.


Same as algorithm #1, but switches back to optimizing the primary objective after a single step with the secondary, during phase (i) of all lateen alternations.

the algorithm alternates between optimizing a primary objective to convergence, then stepping away, using one iteration of the secondary optimizer.


Wrapping EM in an inner loop of a heuristic has a multiplicative effect on the total number of models reestimated prior to termination.

logarithms of the final counts better fit the observed data.


Surprisingly, although Random Indexing takes into account more information than first-order word co-occurrence, it lowered overall performance substantially.

in Table 4, we only present results of using first-order word co-occurrence rather than Random Indexing.


Additionally, of the 150 users who provided a gender cue in their Twitter user description, 105 were female (70%).

females appear more likely to provide explicit indicators about their gender in our corpus.


The language is informal and includes many slang expressions, many of which are unique to the stock tweets community.

many positive and negative expressions common to stock tweets are not found in standard sentiment lexicons.


While buy and sell transactions can be captured reasonably well by a relatively small set of patterns, the examples in  their unique language for reporting these transactions, which must be investigated in order to come by these patterns.

in order to develop a classifier for these tweets, we created a training and test corpora as follows.


Focus accuracy was good indoors, and although the little 3x optical zooms produced sharp shots, the edges were slightly soft on the Canon.

g is a relatively complete and accurate representation; 2) the opinion thread can help to catch global sentiment information, for example the general polarity of a sentence, which is dropped when the opinions are separately represented.


From individual opinion representation, each subgraph of G which takes an opinion expression as root is connected and acyclic.

the connectedness is guaranteed for opinion expressions are connected in opinion thread; the acyclic is guaranteed by the fact that if a modifier is shared by different opinion expressions, the inedges from them always keep (directed) acyclic.


Eq(4) ensures that there will be exactly |V | − 1 edges are chosen.

if the edges corresponding to those non zero y ij is a connected subgraph, y is a well-formed spanning tree.


We observe 60.5% of sentences and 32.1% of opinion expressions contain other modifiers besides "target".

only mining the relations between opinion expressions and evaluation target is actually at risk of inaccurate and incomplete results.


The reason may be that for a modification on opinion expression (r, d k ), we allow d k recursively has its own modifiers (Example 5).

an opinion expression can be a modifier which brings difficulties to classifier.


Besides, the scenario of small corpus is not quite practical for the exponential growing web.

we test IPS on large real world datasets.


According to the statistics in (Yan et al., 2010), the semantic coherent context is about 7 sentences.

we empirically choose k=3 for the examined context window.


Typically this gold function is BLEU (Papineni et al., 2002), though there are several common alternatives (Lavie and Denkowski, 2009;Melamed et al., 2003;Snover et al., 2006;Chiang et al., 2008a).

we want to find a weight vector w such that H w behaves "similarly" to G on a candidate space s. we assume a loss function l s (H w , G) which returns the real-valued loss of using scoring function H w when the gold scoring function is G and the candidate space is s. we may say the goal of tuning is to find the weight vector w that minimizes loss.


In the pairwise approach, the learning task is framed as the classification of candidate pairs into two categories: correctly ordered and incorrectly ordered.

specifically, for candidate translation pair e(i, j) and e(i, j ), we want: We can re-express this condition: optimization reduces to a classic binary classification problem.


This condition also holds when accumulating the bit sequences over a collection of results as long as its elements are selected uniformly from the space of possible results.

the bits generated from a collection of unwatermarked results will follow a binomial distribution with parameter p = 0.5.


However, the same input will be assigned the same output deterministically.

if the distribution of inputs is heavily skewed to certain elements of the input space, the output distribution will not be uniformly distributed.


The experiment was carried out over machines with different configurations and load.

these numbers must be considered as approximate values.


In contrast, HiPDT creates a PDA, which is a more compact representation of the search space and allows efficient intersection with the language model before expansion into an FST.


To illustrate how TM works, consider the following example: Given the Arabic word ,‖من- all possible segmentations are ن( ، )م and .

)من( Given the target words {the, best, man} and the possible mappings for the segments and their probabilities: م ـ = {(m, 0.7), (me, 0.25), (ma, 0.05)} ن = {n, 0.7), (nu, 0.2), (an, 0.1)} من = {(men, 0.4), (man, 0.3), (mn, 0.3)} The only combinations leading valid target words would be: m,an: 0.07), (ma, n: 0.035)} the algorithm would produce the tuple with the highest probability: من( , man, 0.3).


We couldn't fix a minimum score for reliable transliterations to a uniform value for all words, because this would have caused the model to filter out long transliterations.

we tied the threshold to the length of transliterated words.


This slightly improves their system's performance.

there are two scores for each of the 4 We use SMALL CAPS for words, sans serif for MRs and MR constituents (concepts), and italics for non-terminals and Grammars.


These YAGO types are also linked to appropriate WordNet concepts, providing for accurate sense disambiguation.

for any entity argument of a relation we are learning, we obtain sense-disambiguated type information (including super-types, sub-types, siblings etc.


In short, GE training is too slow and too inaccurate early in the active learning process (where labels are more scarce) to be appropriate for our scenario.

we select MNB/Priors to power the DUALIST interface.


Our baseline discriminative model uses first-and second-order features provided in (McDonald et al., 2005;McDonald and Pereira, 2006).

both our tri-sibling model and baseline discriminative model integrate local features that are factored in one hyperedge.


tails(e) contains a node v, the sibling node sib and tri-sibling node tsib of v, moreover, the head of e ′ (head(e ′ )) is the grandparent for v and sib.

in line 5, we can capture tri-sibling and grandsibling information and compute the current inside estimate of Eq.8.


This is written as a shortest path search algorithm with a tropical (real) semiring framework (Mohri, 2002;Huang, 2006).

⊕ denotes the min operater and ⊗ denotes the + operater.


It is only required that the loss for a specific inputoutput pair is fixed throughout training.

the above analysis covers the case where some training instances use an extrinsic loss and others an intrinsic parsing loss.


Our augmented-loss function in this case is a simple binary function: 0 if a parse has the correct root dependency and 1 if it does not.

the algorithm will select the first parse in the k-best list that has the correct root as the proxy to a gold standard parse.


This paper builds upon the insight that a large proportion of the variation between lexical items for a given class of words is systematic.

it should be represented once and applied to a small set of basic lexical units.


To solve this problem, we introduce a syntactic constraint: every multi-word relation phrase must begin with a verb, end with a preposition, and be a contiguous sequence of words in the sentence.

the identification of a relation phrase is made in one fell swoop instead of on the basis of multiple, word-by-word decisions.


as well as lexical aspects (e.g., how many instances of this relation are there?).

as we show in Section 5, systems such as TEXTRUNNER are unable to learn the constraints embedded in REVERB.


Previous work has shown that the frequency of an extraction in a large corpus is useful for assessing the correctness of extractions (Downey et al., 2005).

it is possible a priori that REVERB's gains over previous systems will diminish when extraction frequency is taken into account.


In fact, REVERB's frequency 1 extractions had a precision of 0.75, which TEXTRUNNER could not approach even with frequency 10 extractions, which had a precision of 0.34.

rEVErB is able to return more correct extractions at a higher precision than TEXTrUNNEr, even when redundancy is taken into account.


One fundamental design criterion for our annotation system was the ability to select examples in real time to support, e.g., the interactive annotation experiments presented in this paper.

we could not use the standard MTurk workflow or services like CrowdFlower.


The average staleness of an example in a batch-mode system is half the batch size.

we set the batch size of our comparison system to 25 for NER and to 4 for sentiment.


Furthermore, this performance level is reached at a smaller MTurk cost compared to random sampling.

aL not only reduces annotation costs, but also offers an improvement in absolute performance for these tasks.


Thirdly, newly discovered product attribute values are added back to our seed list.

our original classifier for product attribute extraction can be improved through an expanded seed list.


The best performance was scored at the BOTH=6 setting.

this window size was used for the following experiments.


the evaluation is done on a weekly basis.


One reason for that vulnerability is that Twitter is a kind of communication tool by which a tweet affects other people.

the possibility exists that a few tweets related to "flu" might spread widely, generating an explosive burst of influenza-related tweets.


This task is similar to negation identification, which is a traditional topic, especially in medical fields.

we can find many previous studies of the topic in the relevant literature.


In some social tagging systems, the tags that appear in the resource description are more likely to be selected by users for annotation.

we propose to emphasize the tags in the description by ranking tags as follows where I t (w) is an indicator function which gets value 1 when t = w and 0 when t = w; and γ is the smooth factor with range γ ∈ [0.0, 1.0].


The experimental results on EWTM suggest that, the performance of EWTM is heavily influenced by the length of resource descriptions.

we have to analyze the characteristics of social tagging systems to decide whether to emphasize the tags that appear in the corresponding resource descriptions.


Online social media enable unreliable sources to spread large amounts of unverified information among people (Herman and Chomsky, 2002).

it is crucial to design systems that automatically detect misinformation and disinformation (the former often seen as simply false and the latter as deliberately false information).


In fact, one could intuitively regard a different decomposition path as a different way of interpreting the same semantics.

such a representation could potentially accommodate a wider range of natural language expressions, which all share the same semantics but with very different word choices, phrase orderings, and syntactic structures (like paraphrases).


As discussed in Section 3.1, λ-expressions are represented as packed λ-meaning forests.

in this work, we make use of a bottom-up dynamic programming chart-parsing algorithm that works directly on translating forest nodes into target natural language words.


Therefore, it eliminates the ordering constraint by encompassing exponentially many possible tree structures during both the alignment and decoding stage.

our system obtains significant improvements in both BLEU and 1−TER using the significance test under the paired bootstrap resampling method of Koehn (2004).


Second, events extracted from different documents do not have any connection even if they refer to the same event.

each event is connected with only one document in the bipartite graph and thus cannot propagate its timestamp to other documents unless we perform event coreference resolution.


For example, the extraction "15 people died in this accident" and the extraction "225 people died in this accident" have the same tokens except numerals and they are labeled as a generic event because they are clustered into one group due to high similarity but they in fact refer to different events happening in different years.

if an extraction contains numerals, the feature "NUM" will be included.


If the credibility of the relative temporal relation between i and j is low, propagation from node i to j probably leads to error.

the confidence loss should be much in this case.


On contrary, if the relation is highly credible, it will be less likely that propagation errors occur.


Assume is the optimal propagation path from the propagation source node v 1 to the node v t that leads to the highest confidence of v t , which means that c * Since v 1 is the source node whose timestamp is initially labeled and its confidence is 1, the inequality c A (v 1 ) < c * (v 1 ) cannot hold.

the assumption that c A (v t ) < c * (v t ) cannot be satisfied.


Thus, the assumption that c A (v t ) < c * (v t ) cannot be satisfied.

it can be proved that each node on the bipartite graph must reach the highest confidence it can reach so that the global confidence of the bipartite graph must be optimal when confidence boosting propagation process ends no matter what order time labels are propagated in.


Moreover, data is so redundant that a great number of documents can be connected with events they share.

even if a small number of documents are labeled, the labeled information can be propagated to large numbers of articles through the connections between documents and events according to relative time relations.


When deciding how to interpret [w@nt], if we posit that the intended vowel is /2/, the word is likely to be /w2n/ "one" and the next word begins with /t/; if instead we posit that the vowel is /O/, the word is probably /wOnt/ "want".

inference methods that change only one character at a time are unlikely to mix well.


Although the models with phonetic variability show similar overall segmentation accuracy on noisy data to the original GGJ model, the pattern of errors changes, with less oversegmentation and more un-dersegmentation.

their qualitative performance on variable data resembles GGJ's on clean data, and therefore the behavior of human learners.


We also apply the label to single noun tokens where possible: the only exceptions are compound names ("Sarah Jones") which are treated as single units.

for example, "puppy food" is treated as two words, with puppy animate and food inanimate.


This gives the overall gradient computation: where we sample t k n and update ω k n while moving from left-to-right, and sample s ,k n at each n. Note that although the sequential importance sampler moves left-to-right like a filter, we use only the final weights ω N to compute the expectation.

the resulting expectation is based on the distribution P (s 1:N |t 1:N ), so that no backwards "smoothing" pass (Godsill et al., 2004) is needed to eliminate bias.


We wish to normalized to formal language, which is generally better processed by NLP tools.

for the phrase model, we use the English NIST dataset composed of 8M sentences in English from the news domain to build a 5-gram Kneser-Ney smoothed language model.


This is motivated by the fact that normalizations are transitive.

even if neva cannot be decoded directly to never, we can use nevar as an intermediate step to find the correct normalization.


Furthermore, in the work above, the dictionaries built using these methods assume that lexical variants are mapped to standard forms in a word-toword mapping.

variants such as wanna, gonna and imma are not normalizable, since they are normalized to multiple words want to, going to and I am gonna.


The work on (Han et al., 2012) trains a classifier to decide whether to normalize a word or not, but is still preconditioned on the fact that the word in question is OOV.

lexical variants, such as, 4 and u, with the standard forms for and you, are left untreated, since they occur in other contexts, such as u in u s a.


The expectation follows directly from long-standing and widely influential theories of political competition that are collectively referred to in their simplest form as the "median voter theorem" (Hotelling, 1929;Black, 1948;Downs, 1957).

it is to be expected that when a set of voters that are more ideologically concentrated are replaced by a set who are more widely dispersed across the ideological spectrum, as occurs in the transition between the United States primary and general elections, that candidates will present themselves as more moderate in an effort to capture enough votes to win.


This is based on the observation that token based transduction grammars suffer from a lack of fluency; a consequence of the degree of expressivity they permit.

as a principal part of our investigation we compare the quality of responses generated using a combination of token based rule induction and top-down rule segmenting transduction grammars to those generated by pure token based transduction grammars.


The paper does not introduce novel models or algorithms, but rather, in the authors' words, "show[s] that large back propagation (BP) networks can be applied to real image recognition problems."

although it is has an important role as a landmark neural network success story, it does not score highly in terms of topical influence.


To our knowledge, this is the first study to use distributional representations of recursive modification; therefore we must first determine if the composed AAN vector representations are semantically coherent objects.

for vector analysis, a gold standard of 320 corpus-extracted AAN vectors were selected and their quality was established by inspecting their nearest neighbors.


Clearly, we cannot run these analyses on corpusextracted vectors since the unattested order, by definition, is not seen in our corpus, and therefore we cannot collect co-occurrence statistics for the AAN phrase.

we test our measures of adjective ordering on the model-generated AAN vectors, for all gold rigid order AANs in both orders.


The documents in the two domains share the same universal vocabu- , but the word distributions in the two domains are typically different.

training the classification model directly from the original word feature space V may not generalize well in the target domain.


We consider only those co-occurrences where span is less than a given threshold.

span threshold is a parameter for all the co-occurrence measures being considered.


We did this because i) TaxoLearn extracts terms and glosses from domain corpora in order to create a domain taxonomy; ii) it is one of the few systems which extracts both terms and glosses from specialized corpora; iii) the extracted glossaries are available online.

9 we compared the performance of ProToDoG on two domains for which glossaries were extracted by TaxoLearn, i.e.


Another heuristic applied to our algorithm to reduce the search space for d and v is based on the observation that the average duration of phonetic units is usually no longer than 300 ms.

when computing B * t (i), we only consider speech segments that are shorter than 300 ms to avoid aligning letters to speech segments that are too long to be phonetic units.


These higherĤ values imply that the lexicon induced by our model contains more pronunciation variation than the expert-defined lexicon.

the lattices constructed during the decoding process for our recognizer tend to be larger than those constructed for the oracle baseline, which explains the performance gap between the two systems in Table 2 and Table 3.


This method can reduce the model size because all probabilities are stored into the BASE array.

the VALUE array  contains only backoff weights.


Word IDs in the insertion array are packed to the front in advance.

shift lengths for ordered arrays are often shorter than unordered ones.


The time required for building the model when the original structure was divided into less than 8 parts was too long.

a more efficient building algorithm is essential for advancing this research further.


We can re-write the first and last terms of Equation 9as: As in the previous section, we introduce an alignment variable, a, between t and p, giving: If we consider monotonic alignments, a defines again an alignment between a prefix of the system translation (t p ) and the user prefix, producing the suffix required in IMT (t s ) as the unaligned part.

we can re-write Pr(t | p, a) as: where Equation (16) has been obtained following a naïve Bayes' decomposition.


Note that word-graphs (Ueffing et al., 2002), which are used to represent the search space for phrase-based models, are a special case of hypergraphs in which the maximum arity is one.

hypergraphs allow us to represent both phrase-based and hierarchical systems in a unified framework.


A direct evaluation of the proposed IMT procedures involving human users would have been slow and expensive.

following previous works in the literature (Barrachina et al., 2009;González-Rubio et al., 2010), we used the references in the corpora to simulate the translations that a human user would want to obtain.


Additionally, the similar average BLEU results obtained for the 1000-best translations indicate that word-graphs and hypergraphs encode translations of similar quality.

the IMT systems that use these word-graphs and hypergraphs can be compared in a fair way.


It brings an undesirable gap that separates modeling and learning in an SMT system.

researchers have proposed alternative approaches to learning synchronous grammars directly from sentence pairs without word alignments, via generative models (Marcu and Wong, 2002;Cherry and Lin, 2007;Zhang et al., 2008;DeNero et al., 2008;Neubig et al., 2011;Levenberg et al., 2012) or discriminative models (Xiao et al., 2012).


In the biparsing algorithm, synchronous rules for constructing hyperedges are not required to be in G, but can be any rules that follow the form defined in Chiang (2007).

the biparsing algorithm can discover new rules that are not in G. Then we collect the translation rules discovered in the hypergraph of the reference translation (line 2), which are rules indicated by hyperedges in the hypergraph.


We achieve a further improvement of +0.37 BLEU points.

our training algorithm is able to learn the useful information encoded by the sparse features for translation.


We want to investigate whether the max-margin estimation is able to outperform the max-likelihood estimation method (Xiao et al., 2012).

we carried out experiments to compare them directly.


Using gold mention boundaries can improve the parser performance.

our coreference resolution system can benefit much from using gold mention boundaries (especially the recall).


In the Chinese Treebank, type *pro* constitutes about 20% (Yang and Xue, 2010), and more than 85% of them are anaphoric (Kong and Zhou, 2010).

zero pronouns are very important in bridging the information gap in a Chinese text.


Chinese is a language without natural word delimiters.

chinese Word Segmentation (cWS) is an essential task required by further language processing.


However, the resource of manually labeled training corpora is limited.

semi-supervised learning has become one of the most natural forms of training for CWS.


In the integrated model we only have to apply 1800 updates when we lose the gold sequence during filtering.

in our implementation training a 0order model for Czech takes roughly twice as long as training a 1 st -order model.


Accepts are positive responses to Offers or Counteroffers and are de facto similar to QAP(π 1 , π 2 ) where π 2 is Yes.

the rule is, as for Offer, to update and enrich the CP-net.


3 as can be seen by comparing Row 2 with Row 4.

omitting the arguments of events in evaluations actually appears to allow Turkers to make better judgments.


However, different from regular graphs, each arc in A is considered as a hyperarc in the sense that it can capture relations between any two subsets of nodes: a tail (t i ) and a head (h i ).

a hypergraph is a generalization of a regular graph.


As an alternative to extracting class labels from text, some methods simply import them from human-curated resources, for example from the set of categories encoded in Wikipedia (Remy, 2002).

class labels potentially exhibit higher syntactic diversity.


The prefix "list of" is discarded from each query.

the query "list of software companies that use linux" gives the class label "software companies that use linux".


Even methods using queries as a textual data source still extract class labels from documents using the same extraction patterns (Paşca, 2010).

from the point of view of evaluating class labels, run R dc is a valid representative of previous extraction methods, including (Etzioni et al., 2005;Kozareva et al., 2008;Van Durme and Paşca, 2008;Paşca, 2010;Wu et al., 2012).


Note that, by design, none of the class labels that appear in R ql also appear in runs R qa or R qg .

the intersection between R ql , on one hand, and R qa and R qg , on the other hand, is the empty set.


Note that, since R ql is disjoint from R qa and R qg , none of the class labels already in R ql can be "re-discovered" (generated) again in R qa or R qg .

by experimental design, relative coverage scores of R ql may be relatively difficult to surpass by R qa or R qg taken individually.


The model optimizes W such that f (q, d + ) > f (q, d − ).

the training objective is to minimize the following margin-based loss function: which is also known as contrastive estimation (Smith and Eisner, 2005).


In our case, we treat our problem as: given the occurring context of an entity, retrieving categories corresponding to this entity.

we use context as query q and the categories of this candidate entity as d. We also treat the definition page of an entity as its context, and first train the model with definition pages, because definition pages exhibit more focused topic.


Location name generally is not part of the main topic of one document.

comparing context with its definition is not realistic.


Equipped with suitable indices, a catalog and an annotated corpus let us find "scientists who played some musical instrument", and answer many other powerful classes of queries (Li et al., 2010;Sawant and Chakrabarti, 2013).

accurate corpus annotation has been intensely investigated (Mihalcea and Csomai, 2007;Cucerzan, 2007;Milne and Witten, 2008;Kulkarni et al., 2009;Han et al., 2011;Ratinov et al., 2011;Hoffart et al., 2011).


As regards snippet annotation, Z0 (Zheng et al., 2012) does not specify any mechanism for handling ⊥.


We verified, partly by way of debugging, that there do exist entities e with small degree but a modest number of assigned snippets, for which snippet-to-N (e) matches angus mcdonald, chris robinson, christopher henry, elizabeth provide a boost to type prediction accuracy (about 50%), as compared to T0 (about 20% for these instances).

the flow of information between type and entity assignments is, in principle, bidirectional, in the regime of such entities.


Unfortunately, this is no longer practical for Web corpus C, because 8,217 snippets marked ⊥ would have to be manually inspected and compared with a large number of candidate entities in Freebase.


Our evaluation focuses on sentence-level extractions.

we only apply the steps of each method that perform this task.


Unfortunately, when the learning task is semantically difficult such as in QA, e.g., features have to encode combinations of syntactic and semantic properties.

their extraction modules basically assume the shape of high-level rules, which are, in any case, essential to achieve state-of-the-art accuracy.


In contrast, the PTK fragments can contain any subset of siblings, i.e., PTK allows for breaking the production rules in syntactic trees.

pTK generates an extremely rich feature space, which results in higher generalization ability.


p can better reflects the user's real search intent than the original q, as it presents the structured semantic information needed instead of a simple word string.

a document di can better satisfy a user's information need if it contains corresponding structured semantic information in p. Suppose that Ti is the set of annotated tokens extracted from document di, we compute a re-ranking score, denoted by RScore, for document di as follows: where is an annotated token in p and t is an annotated token in di.


In the conservative re-ranking method, d1, d2, and d5 retain their original positions; while d3 and d4 will be re-ranked according to their RScore.

the final ranking generated by our conservative reranking model should be {d1, d2, d4, d3, d5}, in which the documents are re-ranked among the affected positions.


The other direction is also true -if we run preordering on the source side then the alignment task becomes easier and tends to produce better results.

it can be useful to iterate between generating the alignment and learning a preordering model.


There were very few cases where children were not reordered because of these thresholds, many of them corresponded to bad parses, and they had very little impact on the final scores.

for the 1-step approach we had 6 classifiers: 1 binary classifier for a head and a single child and 5 multi-class classifiers for 3-7 words.


Otherwise, the source-target phrase pair cannot be discovered.

some useful translation relations will be lost.


The divergence between different language families led to a poor performance.

how to select a best pivot language is our future work.


The IWSLT data set is too small to represent the real scenario.

we try our experiment on a more realistic scenario: translating from Chinese to Japanese via English with web crawled data.


More, the parameter training in the third module relies on the CNs constructed from the output of the first module, which increases the instability of the whole system.

an unsupervised multi-objective algorithm may be a good choice allowing for more alignment information.


However, as phrase-based decoding casts translation as a string concatenation problem and permits arbitrary permutations, it proves to be NP-complete (Knight, 1999).

phrase reordering modeling has attracted intensive attention in the past decade (e.g., Tillman, 2004;Zens et al., 2004;Al-Onaizan and Papineni, 2006;Xiong et al., 2006;Koehn et al., 2007;Galley and Manning, 2008;Feng et al., 2010;Green et al., 2010;Bisazza and Federico, 2012;Cherry, 2013).


As a formalism for bilingual modeling of sentence pairs, ITG is particularly well suited to predicting ordering shifts between languages.

a number of authors have incorporated ITG into leftto-right decoding to constrain the reordering space and reported significant improvements (e.g., Zens et al., 2004;Feng et al., 2010).


This potentially alleviates the data sparseness problem since there are usually a large number of reordering training examples available (Xiong et al., 2006).

the MaxEnt ITG model and its extensions (Xiong et al., 2008;Xiong et al., 2010) have achieved competing performance as compared with state-of-the-art phrase-based systems.


The recursive autoencoders, which are trained on reordering examples extracted from word-aligned bilingual corpus, are capable of producing vector space representations for arbitrary multi-word strings in decoding.

our model takes the whole phrases rather than only boundary words into consideration when predicting phrase permutations.


The major challenge of applying ITG to machine translation is to decide when to merge two blocks in a straight order and when in an inverted order.

the ITG reordering model can be seen as a two-category classifier P (o|X 1 , X 2 ), where o ∈ {straight, inverted}.


(2008) find that the MaxEnt classifier with boundary words as features is prone to make wrong predictions for long composed blocks.

they have to impose a hard constraint to always prefer merging long composed blocks in a monotonic way.


As a result, they have to impose a hard constraint to always prefer merging long composed blocks in a monotonic way.

it is important to consider more than boundary words to make more accurate reordering predictions.


The binary tree is composed of a set of triplets in the form of (p → c 1 c 2 ), where p is a parent vector and c 1 and c 2 are children vectors of p. Each child can be either an input word vector or a multi-word vector.

the tree in Figure 2 can be represented as three triplets: In Figure 1, we use recursive autoencoders to generate vector space representations for Chinese and English phrases, which form the atomic blocks for further block merging.


Given a training example set S = {t i = (o i , X 1 i , X 2 i )}, the average reconstruction error on the source side on the training set is defined as  (S; θ), can be computed in a similar way.

the reconstruction error is defined as E rec (S; θ) = E rec,s (S; θ) + E rec,t (S; θ).


Then the cross-entropy error is where o ∈ {straight, inverted}.

as a result, the reordering error is defined as the joint training objective function is where α is a parameter used to balance the preference between reconstruction error and reordering error, R(θ) is the regularizer and defined as 2 (17) as Socher et al.


We find that the number of hypotheses that can be recombined reduces in our system.

we plan to use forest reranking (Huang, 2008) to alleviate this problem.


In this work, we show how the extraction and automatic multi-label classification of any edit in Wikipedia can be handled with a single approach.

we use the 21-category edit classification taxonomy developed in previous work (Daxenberger and Gurevych, 2012).


To illustrate this in a simplified way, take closed up↔closed down in Figure 1, and assume the only feature is the normalized number of matching tokens in the pair.

then this feature firing on the following pairs would have values (the normalization factor is the maximal phrase length): we upweighted feature values by the maximum source or target phrase length to encourage phrasal alignments, in this case closed up ↔closed down:1.0.


With such a heavy imbalance towards only token-based alignment, a phrase-based aligner would learn feature weights that award token alignments more than phrasal alignments.

we synthesized phrasal alignments from continuous monotonic token alignments in these two corpora.


(2013a) showed that the traditional MT bilingual aligner GIZA++ (Och and Ney, 2003) presented weak results on the task of monolingual alignment.

we instead used four other strong baselines: Meteor (Denkowski and Lavie, 2011): a system for evaluating machine translation by aligning MT output with reference sentences.


However, such a procedure is costly as it requires doing inference on all the documents to compute a single gradient update.

we choose a faster stochastic sub-gradient descent (SGD) approach.


"Must-link" constraints encourage a pair of mentions to connect, while "cannot-link" constraints discourage mention pairs from being linked.

the coefficients ρ p associated with "must-link" constraints are positive while ρ p for "cannot-link" constraints are negative.


Consequently, as γ → 0, P r[c ⊙ i; d, w] in Eq.

8 approaches a Kronecker delta function centered on the cluster containing the max-scoring mention, thus reducing to the best-link case of L 3 M. PL 3 M, when tuning the value of γ, is a strictly more general model than L 3 M. Learning with PL 3 M We use a likelihood-based approach to learning with PL 3 M, and first compute the probability P r[C; d, w] of generating a clustering C, given w. We then learn w by minimizing the regularized negative log-likelihood of the data, augmenting the partition function with a loss-based margin (Gimpel and Smith, 2010).


recall of L 3 M are 78.38% and 67.96%, respectively in B 3 ).

the must-link constraints, which aim at improving the recall, do better when optimizing F1.


If object x is in the 10 nearest neighbors of object y, a point is plotted at coordinates (x, y).

there are exactly k = 10 points in each row.


The presence of many warm colored vertical lines suggests that many hub objects appear in the 10 nearest neighbors of other objects that are not in the same cluster as the hubs.

these hubs may have a strong influence on the kNN prediction of other objects.


Centering shifts the origin of the space to the data centroid, and objects similar to the centroid tend to become hubs.

in a sense, centering can be interpreted as an operation that shifts the origin towards hubs.


In their paper, they propose a spectral algorithm based on a generalization of the method of moments for these restricted subclasses.

one open direction for spectral research consists on defining subclasses of context free languages that can be learned (in the strong sense) from observations of yields.


Moreover, a polysemous phrasal verb may express an idiosyncratic sense more or less often than a compositional sense in a particular corpus.

we should expect a corpus-driven system not to classify phrases as strictly idiomatic or compositional, but instead assign a ranking or relative scoring to a set of candidates.


A phrase e aligns to another phrase f if some word in e aligns to some word in f and no word in e or f aligns outside of f or e, respectively.

of this definition, the words within an aligned phrase pair are themselves connected by word-level alignments.


nese/posguide.3rd.ch.pdf In this paper, we employ the commonly used label set {B, I, E, S} for the segmentation part of cross-labels and the label of character can be in the form of {B-T}(T represents POS tag).

each mapping relation t 1 ↔ t 2 can be automatically transformed to four forms: Bt 1 ↔B-t 2 , I-t 1 ↔I-t 2 , E-t 1 ↔E-t 2 and S-t 1 ↔St 2 .


In fact, we give segmentation alignment only to proper names due to the limitation of computing ability.

we can easily build the loose bidirectional mapping functionm for the labels of characters.


Given a sequence x and the expect type of tags T , the merged model iŝ where t(y) is a function to judge the type of output tags; ψ(y) represents the set T .

we redefine the score function as we rewrite the model into a unified formulaŷ = arg max Given an example (x, y),ŷ is denoted as the incorrect label sequence with the highest scorê The margin γ(w; (x, y)) is defined as we calculate the hinge loss ℓ (w; (x, y)), (abbreviated as ℓ w ) by (w; (x, y)), otherwise (13) In round k, the new weight vector w k+1 is calculated by where ξ is a non-negative slack variable, and C is a positive parameter which controls the influence of the slack term on the objective function.


And if the right tag is one of "n,nt,nz/PDD" but not "n/PDD" (for example, "nt/PDD"), which means it is a "NN/CTB", the weight of "NN/CTB" will remain unchanged according to the algorithm (updating "n/PDD" changes the "NN/CTB", but updating "nt/PDD" changes it back).

after multiple iterations, useful features derived from the mapping function are typically receive more updates, which take relatively more responsibility for correct prediction.


Although the loose mapping function are bidirectional between two annotation tagsets, we may also use unidirectional mapping.

we also evaluate the performance when we use unidirectional mapping.


For example, "I feel sick" is universally understood to be a negative situation, even without an explicit expression of negative sentiment.

we must learn to recognize phrases that correspond to stereotypically negative situations.


Ideally, we would like to parse the text and extract verb complement phrase structures, but tweets are often informally written and ungrammatical.

we try to recognize these syntactic structures heuristically using only part-of-speech tags and proximity.


However, personal profiles usually lack organization confronted with the large amount of available information.

it is always a challenge for people to find desired information from them.


After carefully seeing the profiles, we observe that the Experience field contains the most abundant information of a person.

we treat the text of Experience field as the source of summary for each profile.


An objective function is defined based on the joint probability of the factor functions.

the problem of collective personal profile summarization model learning is cast as learning model parameters that maximizes the joint probability of the input continuous dynamic network.


As discussed in subsection 3.3, the average length of summary is about 40 words.

we extract 40 words to construct the summary for each profile.


For global aspects which correspond more to users' interest, they should share a larger proportion in the final storyline than local aspects.

we use an optimization method to determine if a sentence is selected to be an summary sentence or to be discarded based on the multiple local/global aspects and finally get the optimal storyline.


This subset generally grows with the data set.

bNP models address the problem of choosing the number of mixture components or latent factors.


Slonim (2002) claims that the mutual information I(X;X) measures the compactness of the new representation.

the ratedistortion function is a trade-off between the compactness of new representation and the expected distortion.


Thus, nuclearity becomes a property of the CoreSC category, which is indirectly inherited by the sentence.

when creating the CoreSC content model for summaries we addressed the following issues: (i) summary length; (ii) number of sentences from each CoreSC, (iii) the ordering in which sentences from each CoreSC category should appear and (iv) the extraction of sentences according to independent and dependent categories.


(2012) report that the most salient features for classifying CoreSC categories are overall n-grams, verbs and direct objects whereas other features such as the location of the sentence, the neighbouring section headings and whether a sentence contains citations play an important role for some of the categories.

classification into CoreSC categories already provides a selection bias for sentence extraction.


A t-test performed on the population of precision and the population of recalls showed statistical significance at 95% in both cases, with summaries C having a precision of 5% higher and a recall of 1.4-1.6% higher than summaries B (see Table 4).

we can say that CoreSC summaries C are overall better for answering questions than summaries B.


In particular, we aim to determine which language model yields knowledge that is most suitable for use in Computer Vision.

we test a variety of language models and a linguistically mined knowledge base within two computer vision scenarios: Human action recognition : Recognizing <subject, verb, object> triples based on objects (e.g., car, horse) and scenes (the place that the actions occur, e.g., countryside, forest, office) recognized in images.


The ranked list is sorted in the order of the score S. We evaluate human action recognition in terms of this position average over all images, which we call Average Ranking (AR).

we use Average Ranking as our loss-function: Training the energy model involves finding the factors w * F that minimizes the loss: As we have only four parameters to learn in our energy model, we do this by performing an exhaustive search and cross validation.


R  we want statistics from the language models to follow those of the image model, even though not all statistics from images can be reliably measured due to insufficient data.

we measure how well the estimated language models fit the estimated visual distributions using the the χ 2 -distance: where P I and P L are the probability distribution obtained from the image data and language models respectively.


For example, in this dataset with 19 objects and 15 scenes, there are many photos of a person riding a motorbike on rocky mountains as a kind of sport.

the probability of "riding" given "mountain" learnt from the image dataset is high according to the image data (78%) but is uncommon in general.


For the sake of convenience, in this section we assume M is a meta-grammar M = {m}, where each m represents a meta-rule.

for each translation r, there exists a hypergraph H r that represents all possible derivations D r = {d} that can generate rule r. Here, each derivation d is a hyperpath using meta- we can use hypergraph H r to characterize r. Translation rules in G can share nodes and meta-rules in their hypergraphs, so that M is more compact model than G. In the rest of this section, we will introduce three methods to quantify H r as features of rule r. It should be noted that there are more ways to exploit the compact model of M than these three.


The online MIRAs are difficult to parallelize.

similar to the batch MIRA in (Cherry and Foster, 2012), we conduct the batch tuning by repeating the following steps: (a) Decode source sentences (in parallel) and obtain with the one from the previous iteration, (c) Invoke Algorithm 1.


Although Yamaguchi and Tanaka-Ishii (2012) segmented text by language, their data was artificially created by randomly sampling and concatenating text segments (40-160 characters) from monolingual texts.

the language switches do not reflect realistic switches as they occur in natural texts.


In this case for all stress variants of the word we collect prefixes of length 1 through the length of the word, and similarly for suffixes, except that for the stress symbol we treat that together with the vowel it marks as a single symbol.

for the word gorod'a, all prefixes of the word would be g, go, gor, goro, gorod, gorod'a.


Furthermore, an explicit sentence is defined as a sentence containing at least one explicit feature, and an implicit sentence is the sentence only containing implicit features.

the first sentence is an explicit sentence, while the second is an implicit one.


In view of the explicit product feature of each topic, the association of the word and the feature to topic-word distribution should be taken into account.

equation 2 is revised as the following: where C w ′ ,j reflects the correlation of w ′ with the topic j, which is centered on the product feature f z j .


Search is harder on Penn-S due to the larger label set.

as expected, maxviolation update strategies improve most where the search is the hardest and least exact.


Second, we excluded relations that have lower frequency semantic senses or use low frequency connectives.

the model considers only the eight most common semantic senses of relations, which in total account for just less than 90% of the relations in the corpus.


This may well have an explanation based on sentence processing: the intervening attribution phrase 'says Rep. Stark' may serve as a distraction, with the re-sult that the intended coherence relation is harder to infer without a connective.

we include a factor (AttMismatch) indicating if the two arguments are not attributed to the perspective of the same individual.


Specifically, informal observation of the data suggests that there is a tendency for the second argument of an implicit relation to begin with a longer, contentful noun phrase, rather than a pronoun.

our model includes a binary feature (FirstA2Pron) indicating whether the first word in the second argument is a pronoun.


First, there was a significant difference in the variability of judges' responses between items that were incorrectly classified and those that were correct (66% vs. 73%, respectively; two-sample t test: t=2.60, df =73, p<0.02).

as a group the judges were less sure of themselves in those cases in which they incorrectly decided to use or omit the connective, suggesting that either option may have been acceptable.


This is achieved with the help of redundancy of information in this corpus.

we will use a cascade of classifiers and other modules, each of them using the relations deduced by the previous one.


The tree kernel classifier (Moschitti, 2006) based on support vector machines uses a kernel function between two trees, affording a comparison between their substructures.

a tree kernel-based scope detection approach with structured syntactic parse tree is employed.


But in Clinical Reports sub-corpus, it is shorter than that in Abstracts and Full Papers.

looking for the effective features in short sentences is especially important for improving the robustness for scope detection.


The scope involves the subject "An age-related decrease".

we have built a discriminative classifier for each kind of POS of cues, and then explored and selected the most compatible features for each classifier.


The scope classifier may result in discontinuous blocks, as each token may be classified inside or outside the scope.

we perform the rule based post-processing algorithm proposed by Morante et al (2008) to obtain continuous scopes.


The saved coordinates are updated if the page layout is changed due to resize window event or AJAX action.

of this instrumentation, for each page visit we know the searcher's intent (question), a search engine query that the user issued, a URL and HTML page, the bounding boxes of each word in the HTML text, and all of the searcher actions, e.g., mouse movement coordinates, mouse clicks, and scrolling.


One might naturally ask whether there is greater potential upside from this channel model.

we turn to measuring the upper bound on accuracy for the joint approach as a whole.


In future work we plan to directly learn representations of the source-side during training of the joint model.

the model itself can decide which encoding is best for the task.


The translation quality is often unsatisfactory when translating texts from a specific domain using a general model that is trained over a hotchpotch of bilingual corpora.

domain adaptation is crucial for SMT systems to achieve better performance.


The key advantage of MTL is to enable implicit data sharing and regularization.

it often leads to a better model for each task.


This criterion biases towards sentence pairs that are like the in-domain corpus but unlike the general-domain corpus.

the sentence pairs with lower scores (larger differences) are presumed to be better.


In large-scale real world SMT systems, practical domain adaptation techniques should target more domains rather than just one due to heterogeneous input.

we use a web crawler to collect monolingual webpages of N domains from web portal sites, for both the source language and the target language.


The basic idea of the objective function is to minimize the sum of loss functions for all the domains, rather than one domain at a time.

by adjusting the in-domain and general-domain feature weights, the translation quality is expected to be good across different domains.


However, this is not always the case for real world SMT systems.

to apply our approach in real applications, the domain information needs to be identified automatically.


Using these per-document marginals provides additional information to the learning function but would overwhelm a common solver.

we present a sequential learning method for approximately matching the large set of document pair marginal distributions.


Backtracing applies only for rules having at least two non-terminals.

the total height and width penalty for this derivation are 3 and 6 respectively.


This feature simply counts the number of reordering rules, where the non-terminals in source and target sides are reordered.

r = rule(D, ), where rule(D, ) is the number of reordering rules in D. Similar to width and height, this feature is applied for rule having at least two non-terminals.


In LR-decoding, we compute distortion for rules even though we are yet to translate some of the sub-spans.

our approach computes the distortion incrementally for the untranslated sub-spans which are later added.


Train best div Test best 51.2 51.7 div 50.5 51.8 Table 6: Comparing M -best and diverse lists for training/testing (AR→EN, all features).

far, when training the reranker on M -best lists, we tested it on M -best lists, and similarly for diverse lists.


The number of sentence pairs in this subset is 1,032, which contains similar number of words to our 616-sentence dev set (since reachable sentences are much shorter).

it is reasonable to compare different learning algorithms in terms of speed and performance.


We then produce a similarity vector sv (called s-vector) for d. sv consists of a set of similarity values between document d (in a dvector) and query q (in a d-vector): where Sim is a similarity function consists of a set of similarity measures.

the d-vector for document d in the document space is transformed to an s-vector sv for d in the similarity space.


However, since they are randomly selected from a large number of userids, the probability that two sampled userids belong to the same person is very small.

it should be safe to assume that each userid here represents a unique author.


we designed three new baselines: TSL: This baseline is based on the traditional supervised learning (TSL).


break down performance by language, examine to what extent its results were due to better performance on some languages), or indeed discuss fully which languages were present in their sample.

little can be inferred from Burger et al.


Each language considered in this paper belongs to a different language family: French to Indo-European, Turkish to Altaic, Japanese to Japonic, and Indonesian to Austronesian.

these languages are completely genetically unrelated, by definition.


Note that "k-top X features" (e.g., k-top hashtags) refers to the k most discriminating items of that type for each label (i.e., Male/Female).

k-top words is actually 2k features: the k words most associated with males and the k words most associated with females.


As tokenization does involves some languagespecific processing, its use here somewhat undermines the objective set out for this project.

we report the accuracy achieved for both untokenized and tokenized Japanese tweets.


Note that to conduct the cross-fold evaluation, new models (and hence different features) were recomputed for each fold.

the features reported are slightly different from those that might have appeared in the models for a given fold.


The terms men use are mostly highly informal, including the slang term lu (you) and the English borrowing bro; the address terms women use are mostly medium-formality, such as aku (I) and kamu (you).

women seem to be using "more polite" self-reference and address terms than men on average on Indonesian Twitter, in line with the more general tendency for women to use polite forms more frequently than men crossculturally (Holmes, 1995).


As with Indonesian, a concern is that topics represent a biased sample of the population.

we tested a classifier with soccer-specific terms removed, and again found no difference in accuracy (86% vs. 87%).


The issue was that they employed highly irregular spellings that our implementation was not able to pick up.

with additional refinement, it may be possible to improve the suis-construction coverage further, well beyond 80%.


In this case, in order to manage the consensus with non-functional grammars, we decompose such a tree into two sets: a set of anchored categories X s and a set of anchored function labels F s .

a tree T can be described by means of two boolean vectors: • z(T ) indexed by anchored categories X s , z(T ) Xs = 1 if there exists a function label F so that (X, F ) s is in T , and 0 otherwise; • ζ(T ) indexed by anchored function labels F s , ζ(T ) Fs = 1 if there exists a category X so that (X, F ) s is in T , and 0 otherwise.


In contrast, the LTH tool makes use of function label information in phrase structure trees.

their availability results in only a moderate improvement in LAS for the Stanford dependencies and a very striking improvement for the LTH dependencies.


NLP models often have millions of mainly sparsely attested features.

balancing overfitting versus underfitting through good weight regularization remains a key issue for achieving optimal performance.


However, our quadratic approximation extends easily to the multiclass case and to structured prediction in general, while Gaussian dropout does not.

it appears that our approximation presents a reasonable trade-off between computational efficiency and prediction accuracy.


Given that many words contain more than one character, a wordbased model can examine a wider context.

the contextual dependency information encoded in a character-based model is generally weaker than in a word-based model.


Confidence scores between the two segmenters, however, are not directly comparable.

we rank the sentences by their confidence scores in each segmenter respectively, and calculate the rank difference between the two segmenters.


In our implementation, a dictionary is extracted from the segmented training set.

the word-based model suffers a lot when the training data is small.


Specifically, since a normalized-case structure represents the same meaning in the same representation, normalized-case analysis is useful for recognizing textual entailment and information retrieval.

we need a system that first analyzes surface cases and then alternates the surface cases with normalized-cases.


For example, the instances of the ga-case of the case frame " -5 (be pushed down-5)" and the wo-case of the case frame " -4 (push down-4)," which are considered to be aligned and represent patient, are similar.

we exploit semantic similarity sim SEM between the instances of the corresponding cases.


the alignment A 1 would be selected by considering the case distribution similarity.


only 27.38% of ni-case is alternated with ga-case.

when we can use development data, we exploit a weighting factor f P P (A) that is determined on the development data and takes into account the preference of alternation patterns.


Of course, these case frames contain improper ones, that is, several frames mix several meanings or usages of the predicates.

it is difficult to evaluate the acquired knowledge itself.